Monotone data augmentation algorithm for longitudinal continuous, binary and ordinal outcomes: a unifying approach

TL;DR

This paper extends the monotone data augmentation algorithm to handle longitudinal binary and ordinal outcomes within a unified framework, improving efficiency and facilitating complex prior handling.

Contribution

It introduces a unifying MDA approach for continuous, binary, and ordinal outcomes in longitudinal data, with a parameter expansion and Metropolis-Hastings strategies.

Findings

Enhanced sampling efficiency for mixed outcomes

Effective handling of complex priors in Bayesian models

Numerical examples demonstrate improved imputation accuracy

Abstract

The monotone data augmentation (MDA) algorithm has been widely used to impute missing data for longitudinal continuous outcomes. Compared to a full data augmentation approach, the MDA scheme accelerates the mixing of the Markov chain, reduces computational costs per iteration, and aids in missing data imputation under nonignorable dropouts. We extend the MDA algorithm to the multivariate probit (MVP) model for longitudinal binary and ordinal outcomes. The MVP model assumes the categorical outcomes are discretized versions of underlying longitudinal latent Gaussian outcomes modeled by a mixed effects model for repeated measures. A parameter expansion strategy is employed to facilitate the posterior sampling, and expedite the convergence of the Markov chain in MVP. The method enables the sampling of the regression coefficients and covariance matrix for longitudinal continuous, binary and ordinal outcomes in a unified manner. This property aids in understanding the algorithm and developing computer codes for MVP. We also introduce independent Metropolis-Hasting samplers to handle complex priors, and evaluate how the choice between flat and diffuse normal priors for regression coefficients influences parameter estimation and missing data imputation. Numerical examples are used to illustrate the methodology.